Workshop goals

What you will need

What is R?

R is a software environment for statistical computing and graphics. Using R you can do rigorous statistical analysis, clean and manipulate data, and create publication-quality graphics.

clustering map

Popularity of R

Source: Stephen Cass, “The 2016 Top Programming Langauges”, IEEE Spectrum

R packages

Packages are programs that you import into R to help make tasks easier. The most popular R packages for working with data include dplyr, stringr, tidyr, and ggplot2.

How to find a package

There’s no easy way (yet) for new R users to find R packages that they might need. People are working on this problem. In the meantime, consult the following list or ask a Librarian!

Resources include:

Core R functions for plotting

You can create graphs in R without installing a package, but packages will allow you to create better visualizations that are any of the following:

ggplot2

ggplot2 is the most popular visualization package for R. It’s the best all-purpose package for creating many types of 2-dimensional visualizations.

Source: r-statistics.co

highcharter

Highcharter is an R package known as an htmlwidget, which allows you to use popular javascript packages for visualization and create interactive web charts. Highcharter is the R interface to the popular highchartsJS, a charting library in javascript. It’s free to use highcharter unless you are using it for a commercial or government purpose.

data(citytemp)

hc <- highchart() %>% 
  hc_xAxis(categories = citytemp$month) %>% 
  hc_add_series(name = "Tokyo", data = citytemp$tokyo) %>% 
  hc_add_series(name = "London", data = citytemp$london) %>% 
  hc_add_series(name = "Other city",
                data = (citytemp$tokyo + citytemp$london)/2)

hc

leaflet

Leaflet is popular among web developers for creating interactive web maps. It’s an htmlwidget for R based on LeafletJS.

m <- leaflet(options = leafletOptions(zoomControl = FALSE, dragging=FALSE, minZoom = 15, maxZoom = 15)) %>%
  addTiles() %>%  # Add default OpenStreetMap map tiles
  addMarkers(lng=-78.6697, lat=35.7876,
    popup="Hello World!")
m  # Print the map

plotly

Plotly is somewhat new and is making inroads among data scientists for making interactive visualizations.

p <- plot_ly(economics, x = ~date, y = ~unemploy / pop)
p

Deciding on the right package

ggplot2: the most important package to learn first

ggplot2 was created on the principles of the Layered Grammar of Graphics (2010), by Hadley Wickham and based of off work from Wilkinson, Anand, & Grossman (2005) and Jaques Bertin (1983).

Essentially: graphs are like sentences you can construct, and they have a grammar. The grammar of graphics consists of the following:

at least one layer:

plus the following: * scale
* coordinate system
* facet (optional)

These components make up a graph.

Open script.R file

Download the following file: script.R Click the blue download button
Open RStudio. File > Open File…
Select the script.R file that you just downloaded (probably in your Downloads folder) Click Open

Get to know the data

Let’s see an example of a simple graph created with ggplot. We are going to use the mpg data set about different cars and their properties.

Exercise #1: In your script file, run ?mpg to learn more about this dataset. To run the code, highlight it and then click Run. (shortcut keys: Mac: command + Enter, Windows: CTRL + Enter)

?mpg

Exercise #2: Run head(mpg) to see the first few rows of the data.

head(mpg)
## # A tibble: 6 x 11
##   manufacturer model displ  year   cyl      trans   drv   cty   hwy    fl
##          <chr> <chr> <dbl> <int> <int>      <chr> <chr> <int> <int> <chr>
## 1         audi    a4   1.8  1999     4   auto(l5)     f    18    29     p
## 2         audi    a4   1.8  1999     4 manual(m5)     f    21    29     p
## 3         audi    a4   2.0  2008     4 manual(m6)     f    20    31     p
## 4         audi    a4   2.0  2008     4   auto(av)     f    21    30     p
## 5         audi    a4   2.8  1999     6   auto(l5)     f    16    26     p
## 6         audi    a4   2.8  1999     6 manual(m5)     f    18    26     p
## # ... with 1 more variables: class <chr>

Exercise #3: ggplot syntax

The graph below uses ggplot2 to look for correlation between a car’s engine displacement and highway mileage.

Run the following code in your script file

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))

Exercise #4: Practice

Make a scatterplot with cyl mapped to the x-axis and hwy mapped to the y-axis.

Solution to #4

ggplot(data= mpg) + geom_point(mapping = aes(x=cyl, y=hwy))

Exercise #5: Mapping a variable to color

Make a scatterplot of disp=x and hwy=y with class mapped to the color aesthetic. Run:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Exercise #6: Make the same scatterplot as the previous example, but map drv to color.

Solution to #6

The type of drive system the car has (4-wheel, rear-wheel, and front-wheel) is mapped to color.

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = drv))

Exercise #7: Aesthetic parameters

Variables can be mapped to the following aesthetic parameters. If you are publishing in b/w, and can’t use color, you might want to use size or shape:

Substitute another aesthetic in place of color. Run the code:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy, color = drv))

Exercise #8: Faceting

Facets are a way to create multiple smaller charts, or subplots, based on a variable. Run this code to see what faceting does:

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Exercise #9: Practice Faceting

Substitute class for another variable in the dataset. Ex: trans, drive, or cyl

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

Exercise 10: facet_grid

Facet grids allow for an extra dimension of faceting. Run this code in your script to see what facet_grid() does:

ggplot(data = mpg) + geom_point(mapping = aes(x = displ, y = hwy)) + 
  facet_grid(class ~ cyl)

Exercise 11: Apply what you’ve learned!

Now create a new scatter plot with the dataset diamonds using ggplot2. Refer to previous code examples for assistance.

head(diamonds)
## # A tibble: 6 x 10
##   carat       cut color clarity depth table price     x     y     z
##   <dbl>     <ord> <ord>   <ord> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 1  0.23     Ideal     E     SI2  61.5    55   326  3.95  3.98  2.43
## 2  0.21   Premium     E     SI1  59.8    61   326  3.89  3.84  2.31
## 3  0.23      Good     E     VS1  56.9    65   327  4.05  4.07  2.31
## 4  0.29   Premium     I     VS2  62.4    58   334  4.20  4.23  2.63
## 5  0.31      Good     J     SI2  63.3    58   335  4.34  4.35  2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336  3.94  3.96  2.48

Example:

ggplot(data = diamonds) + 
  geom_point(mapping = aes(x = carat, y = price, color=cut)) + facet_wrap(~cut, nrow=2)

Exercise 12: Adding multiple layers to a graph

So far we have just worked with one chart layer. But it’s possible to add more layers to charts in ggplot2, and style those layers individually if you want to. Here’s an example using geom_smooth(), which fits a model to the data. Notice that the color variable is only applied to the scatter points, and not the line.

ggplot(data = diamonds) + 
  geom_point(mapping = aes(x = carat, y = price, color=cut)) +
  geom_smooth(mapping = aes(x = carat, y = price))

## Exercise 13: Coding for efficiency To reduce typing, put the aesthetics that are shared by all layers (global) in the ggplot() function. Put unique aesthetics in the geom() functions that are specific to that (local) layer only. Run this code to see what happens:

ggplot(data = diamonds, mapping = aes(x = carat, y = price)) + 
  geom_point(aes(color=cut)) +
  geom_smooth()

Exercise 14: Practice putting aesthetics in ggplot() function

Now use the short hand method to make the following code more efficient. Type your answer in the script:

ggplot(data = diamonds) + 
  geom_point(mapping = aes(x = carat, y = price, color=clarity)) +
  geom_smooth(mapping = aes(x = carat, y = price))

Solution to #14

ggplot(data = diamonds, mapping = aes(x=carat, y=price)) + 
  geom_point(aes(color=clarity)) +
  geom_smooth()

It’s also possible to write it even more efficiently:

ggplot(diamonds, aes(carat, price)) + geom_point(aes(color=clarity)) + geom_smooth()

Exercise 15: Position adjustments

The previous chart has some issues with data points overlapping, and also too many spaghetti lines. We can use alpha, which is an aesthetic, and position to help reduce some of that overlap. Remember that position is one of the elements in the layered grammar of graphics.

alpha - use to make points more transparent so you can see points underneath position: takes values identity, dodge, fill, or jitter

ggplot(data =diamonds, mapping = aes(carat, price)) + geom_point(mapping = aes(color=clarity, alpha=1/5), position="jitter") + geom_smooth()

Other chart types in ggplot2

To make a scatter plot, we used the geom_point() function. You can use different geom functions to make other chart types. Here are just a few examples of the many geom functions:

Each function can take certain parameters. To learn more about a function, you can type ?+name of function, for example, ?geom_bar

Bar Chart Example

Use geom_bar() function to create a bar chart. Without a specified y variable, bar charts in ggplot calculate count, a new value. count is a statistical tranformation of your data that ggplot2 automatically does.

Exercise 16. Run this example of a bar chart:

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut))

Exercise 17: Bar Chart Practice

Create a bar chart where x=clarity.

Solution to #17

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = clarity))

Statistical transformations

stat is another part of the grammar of graphics: essentially, a statistical tranformation of the data. Each layer has one.

geom functions each have their own default stat, or statistical transformation, measures, that transform the data. * geom_bar has a default stat called count * geom_histogram has bin …and so on

It’s possible to change these default settings, but normally, you don’t need to. Notice that we didn’t have to set stat to a value in order to create a bar chart.

To find out how to do this, type ?+package name in the console. Example ?geom_bar()

Creating charts for communication